Publications related to A Variational Framework for Structure from Motion in Omnidirectional Image Sequences

Related publications (27)

Advancing Self-Supervised Deep Learning for 3D Scene Understanding

Recent advancements in deep learning have revolutionized 3D computer vision, enabling the extraction of intricate 3D information from 2D images and video sequences. This thesis explores the application of deep learning in three crucial challenges of 3D com ...

EPFL2024

Anthony C Davison and Igor Rodionov's contribution to the Discussion of 'Estimating means of bounded random variables by betting' by Waudby-Smith and Ramdas

Igor Rodionov, Anthony Christopher Davison

We derive confidence intervals (CIs) and confidence sequences (CSs) for the classical problem of estimating a bounded mean. Our approach generalizes and improves on the celebrated Chernoff method, yielding the best closed-form "empirical-Bernstein" CSs and ...

Oxford Univ Press2024

Aggregating Spatial and Photometric Context for Photometric Stereo

David Honzátko

Photometric stereo, a computer vision technique for estimating the 3D shape of objects through images captured under varying illumination conditions, has been a topic of research for nearly four decades. In its general formulation, photometric stereo is an ...

EPFL2024

3D reconstruction of curvilinear one-dimensional objects viewed in transmission electron microscopy

Gulnaz Ganeeva

To obtain a more complete understanding of material microstructure at the nanoscale and to gain profound insights into their properties, there is a growing need for more efficient and precise methods that can streamline the process of 3D imaging using a tr ...

EPFL2023

State of the Art in Dense Monocular Non-Rigid 3D Reconstruction

Pascal Fua

3D reconstruction of deformable (or non-rigid) scenes from a set of monocular 2D image observations is a long-standing and actively researched area of computer vision and graphics. It is an ill-posed inverse problem, since-without additional prior assumpti ...

WILEY2023

Exploring Novel Modalities for Optical Diffraction Tomography

Amirhossein Saba Shirvan

In the past decade, optical diffraction tomography has gained a lot of attention for its ability to create label-free three-dimensional (3D) images of the refractive index distribution of biological samples using scattered fields measured through holograph ...

EPFL2023

Detecting Anomalies and Obstacles in Road Scenes

Krzysztof Maciej Lis

We address the problem of segmenting anomalies and unusual obstacles in road scenes for the purpose of self-driving safety. The objects in question are not present in the common training sets as it is not feasible to collect and annotate examples for every ...

EPFL2023

Confidence Matters: Applications to Semantic Segmentation

Prabhu Teja Sivaprasad

The successes of deep learning for semantic segmentation can in be, in part, attributed to its scale: a notion that encapsulates the largeness of these computational architectures and the labeled datasets they are trained on. These resource requirements hi ...

EPFL2023

Robust Isometric Non-Rigid Structure-From-Motion

Shaifali Parashar

Non-Rigid Structure-from-Motion (NRSfM) reconstructs a deformable 3D object from keypoint correspondences established between monocular 2D images. Current NRSfM methods lack statistical robustness, which is the ability to cope with correspondence errors. T ...

IEEE COMPUTER SOC2022

Learning to Represent and Reconstruct 3D Deformable Objects

Jan Bednarík

Representing and reconstructing 3D deformable shapes are two tightly linked problems that have long been studied within the computer vision field. Deformable shapes are truly ubiquitous in the real world, whether be it specific object classes such as human ...

EPFL2022

Weakly Supervised Volumetric Image Segmentation with Deformed Templates

Pascal Fua, Pamuditha Udaranga Wickramasinghe, Mian Akbar Shah

There are many approaches to weakly-supervised training of networks to segment 2D images. By contrast, existing approaches to segmenting volumetric images rely on full-supervision of a subset of 2D slices of the 3D volume. We propose an approach to volume ...

Springer2022

Encoder-Decoder Models for Human Segmentation and Motion Analysis

Isinsu Katircioglu

Detecting people from 2D images and analyzing their motion in 3D have been long standing computer vision problems central to numerous applications such as autonomous driving and athletic training. Recently, with the availability of large amounts of trainin ...

EPFL2022

2.5 Hz sample rate time-domain near-infrared optical tomography based on SPAD-camera image tissue hemodynamics

Edoardo Charbon, Scott Anthony Lindner

Tim-domain near-infrared optical tomography (TD NIROT) techniques based on diffuse light were gaining performance over the last years. They are capable of imaging tissue at several centimeters depth and reveal clinically relevant information, such as tissu ...

OPTICAL SOC AMER2022

Synthesis and Analysis of 3D shapes with Geometric Deep Learning in Computer-Aided Engineering

Edoardo Remelli

In this thesis, we advocate that Computer-Aided Engineering could benefit from a Geometric Deep Learning revolution, similarly to the way that Deep Learning revolutionized Computer Vision. To do so, we consider a variety of Computer-Aided Engineering pr ...

EPFL2022

The long cross-over dynamics of capillary imbibition

Ignacio Pagonabarraga Mora

Spontaneous capillary imbibition is a classical problem in interfacial fluid dynamics with a broad range of applications, from microfluidics to agriculture. Here we study the duration of the cross-over between an initial linear growth of the imbibition fro ...

Cambridge University Press2022

Unambiguous DNFs and Alon-Saks-Seymour

Mika Tapani Göös, Siddhartha Jain

We exhibit an unambiguous k-DNF formula that requires CNF width (Omega) over tilde (k(2)), which is optimal up to logarithmic factors. As a consequence, we get a near-optimal solution to the Alon-Saks-Seymour problem in graph theory (posed in 1991), which ...

IEEE COMPUTER SOC2022

Bragg edge tomography characterization of additively manufactured 316L steel

Roland Logé, Christian Leinenbach, Nikola Kalentics

In this work we perform a neutron Bragg edge tomography of stainless steel 316L additive manufacturing samples, one as built via standard laser powder bed fusion, and one using the novel three-dimensional (3D) laser shock peening technique. First, we consi ...

AMER PHYSICAL SOC2022

Advanced Techniques in Optical Diffraction Tomography

Ahmed Bassam Sayed Ayoub Mohamed Emam

In this thesis, we study the 3 challenges described above. First, we study different reconstruction techniques and assess the fidelity of each reconstruction results by means of structured illumination and phase conjugation. By reconstructing the 3D refrac ...

EPFL2022

Quantization for Distributed Processing and Learning of Structured Data

Isabela Cunha Maia Nobre

In the domains of machine learning, data science and signal processing, graph or network data, is becoming increasingly popular. It represents a large portion of the data in computer, transportation systems, energy networks, social, biological, and other s ...

EPFL2022

Numerical and experimental investigation of spiral-grooved gas journal bearings

Elia Iseli

Small-scale turbomachinery is increasingly used in carbon-free energy conversion systems, such as commercial or domestic scale heat pumps, fuels cells for transportation and waste heat recovery. The usage of aerodynamic bearings allows the design of compac ...

EPFL2022